





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class       lowerCAmelCase__(			unittest.TestCase     ):






       '''simple docstring'''


       @property
       def      UpperCamelCase_      (   self				)      ->  Optional[Any]:
            torch.manual_seed(0				)
            _SCREAMING_SNAKE_CASE							:						int         =				UNetaDModel(
                block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    sample_size=3_2     ,    in_channels=3     ,    out_channels=3     ,    down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,    up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,    )
            return model







       def      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						int         =				self.dummy_uncond_unet
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				ScoreSdeVeScheduler()

            _SCREAMING_SNAKE_CASE							:						List[Any]         =				ScoreSdeVePipeline(unet=__snake_case     ,    scheduler=__snake_case				)
            sde_ve.to(__snake_case				)
            sde_ve.set_progress_bar_config(disable=__snake_case				)

            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				torch.manual_seed(0				)
            _SCREAMING_SNAKE_CASE							:						Tuple         =				sde_ve(num_inference_steps=2     ,    output_type="numpy"     ,    generator=__snake_case				).images

            _SCREAMING_SNAKE_CASE							:						Dict         =				torch.manual_seed(0				)
            _SCREAMING_SNAKE_CASE							:						Any         =				sde_ve(num_inference_steps=2     ,    output_type="numpy"     ,    generator=__snake_case     ,    return_dict=__snake_case				)[
                0
            ]

            _SCREAMING_SNAKE_CASE							:						Tuple         =				image[0, -3:, -3:, -1]
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 3_2, 3_2, 3)
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

            assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2





@slow
@require_torch
class       lowerCAmelCase__(			unittest.TestCase     ):






       '''simple docstring'''


       def      UpperCamelCase_      (   self				)      ->  str:
            _SCREAMING_SNAKE_CASE							:						Dict         =				"""google/ncsnpp-church-256"""
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				UNetaDModel.from_pretrained(__snake_case				)

            _SCREAMING_SNAKE_CASE							:						int         =				ScoreSdeVeScheduler.from_pretrained(__snake_case				)

            _SCREAMING_SNAKE_CASE							:						Any         =				ScoreSdeVePipeline(unet=__snake_case     ,    scheduler=__snake_case				)
            sde_ve.to(__snake_case				)
            sde_ve.set_progress_bar_config(disable=__snake_case				)

            _SCREAMING_SNAKE_CASE							:						List[str]         =				torch.manual_seed(0				)
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				sde_ve(num_inference_steps=1_0     ,    output_type="numpy"     ,    generator=__snake_case				).images

            _SCREAMING_SNAKE_CASE							:						Any         =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 2_5_6, 2_5_6, 3)

            _SCREAMING_SNAKE_CASE							:						int         =				np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

            assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2



'''simple docstring'''


import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCAmelCase_						:  List[Any]				  =						'.'


if __name__ == "__main__":
				UpperCAmelCase_						:  Any				  =						os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
				UpperCAmelCase_						:  Dict				  =						[]
				UpperCAmelCase_						:  Union[str, Any]				  =						[]
				with open(doctest_file_path) as fp:
								for line in fp:
												UpperCAmelCase_						:  int				  =						line.strip()
												UpperCAmelCase_						:  str				  =						os.path.join(REPO_PATH, line)
												if not (os.path.isfile(path) or os.path.isdir(path)):
																non_existent_paths.append(line)
												all_paths.append(path)
				if len(non_existent_paths) > 0:
								UpperCAmelCase_						:  int				  =						'\n'.join(non_existent_paths)
								raise ValueError(F"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
				if all_paths != sorted(all_paths):
								raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




